In this technical note, we consider adaptive control of single input uncertain nonlinear systems in the presence of input saturation and unknown external disturbance. By using backstepping approaches, two new robust adaptive control algorithms are developed by introducing a well defined smooth function and using a Nussbaum function. The Nussbaum function is introduced to compensate for the nonlinear term arising from the input saturation. Unlike some existing control schemes for systems with input saturation, the developed controllers do not require assumptions on the uncertain parameters within a known compact set and a priori knowledge on the bound of the external disturbance. Besides showing global stability, transient performance is also established and can be adjusted by tuning certain design parameters.

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Robust Adaptive Control of Uncertain Nonlinear Systems in the Presence of Input Saturation and External Disturbance

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints

A command filtered backstepping approach is presented that uses adaptive function approximation to control unmanned air vehicles. The controller is designed using three feedback loops. The command inputs to the airspeed and flight-path angle controller are x c , γ c , V c and the bounded first derivatives of these signals. That loop generates comand inputs μ c , α c for a wind-axis angle loop. The sideslip angle command β c is always zero. The wind-axis angle loop generates rate commands P c , Q c , R c for an inner loop that generates surface position commands. The control approach includes adaptive approximation of the aerodynamic force and moment coefficient functions. The approach maintains the stability (in the sense of Lyapunov) of the adaptive function approximation process in the presence of magnitude, rate, and bandwidth limitations on the intermediate states and the surfaces.

Backstepping-Based Flight Control with Adaptive Function Approximation

https://www.ics.forth.gr/bioloch/internal/papers/mas_tactile.pdf

Tactile sensing technology for minimal access surgery––a review

In the application of adaptive e ight control, signie cant issues arise due to limitations in the plant inputs, such as actuator displacement limits, actuator rate limits, linear input dynamics, and time delay. A method is introduced that allows an adaptive law to be designed for the system without theseinput characteristicsand then to be applied to the system with these characteristics, without affecting adaptation. This includes allowing correct adaptation while the plant input is saturated and allows the adaptation law to function when not actually in control of the plant. To apply the method, estimates of actuator positions must be found. However, the adaptation law can correct for errors in these estimates. Proof of boundedness of system signals is provided for a single hidden-layer perceptron neural network adaptive law. Simulation results utilizing the methods introduced for neural network adaptive control of a reusable launch vehicle are presented for nominal e ight and under failure cases that require considerable adaptation.

Limited authority adaptive flight control for reusable launch vehicles

This paper deals with the problem of adaptively controlling a linear time-invariant plant in the presence of constraints on the input amplitude. We introduce a new adaptive algorithm which ensures that the plant can be locally stabilized. In addition to the standard assumptions which are required for adaptive control in the ideal case, an upper bound on the plant control parameter is required to be known. The results are evaluated by simulation studies. >

/pdf/adaptive-control-in-the-presence-of-input-constraints-31vvjiv7ek.pdf

Adaptive control in the presence of input constraints

Discrete-time adaptive control in the presence of input constraints

This paper deals with the problem of adaptively controlling a linear time-invariant plant in the presence of constraints on the input amplitude. We introduce a new algorithm which ensures that the adaptive controller leads to bounded solutions when the initial conditions of the adaptive system lie within a compact set. The results are applied to the problem of manipulation using compliant fingerpads.

Adaptive Control in the Presence of Input Constraints

Human and robot tactile sensing can be accomplished by arrays of mechanosensors embedded in a deformable medium. When an object comes in contact with the surface of the medium, information about the shape of the medium's surface and the force distribution within the region of contact on the surface is available in the stress/strain states at the sensor locations within the medium. The mechanosensors transduce these signals, and the problem for the central processor is to reliably and efficiently infer the contact state and the object properties on the surface from the sensor signals. In this paper, a frequency-domain approach is used to solve the problem of encoding and decoding mechanosensory information. A Solution to the encoding problem is given with the medium modeled as a general three-dimensional infinite half-space composed of a linear elastic material and subjected to three-dimensional loads. It is shown that considerations of symmetry and bandwidth of sensor response uniquely determine the optim...

Encoding and Decoding of Static Information in Tactile Sensing Systems

An active instrumented object was built to investigate the human ability to grasp and hold the object against gravity during rapid contractions of the object. Initial grasp force was shown to be the dominant variable that determined whether the object was held or dropped. Other factors such as the friction of the interface or the contraction velocities used (130 mm/s or higher) were not found to have a significant effect. A third order lumped parameter model of the finger was proposed and the parameters of the model corresponding to the stiffness and damping of the fingerpad and muscle together with finger mass were identified using the recorded force and displacement data. This model of the finger was shown to predict the experimental data quite well, indicating that the subjects' fingers are effectively passive during rapid haptic events that occur within 60 to 80 ms.

S. P. Kárason

Papers

Adaptive control in the presence of input constraints

Discrete-time adaptive control in the presence of input constraints

Adaptive Control in the Presence of Input Constraints

Encoding and Decoding of Static Information in Tactile Sensing Systems

Passive human grasp control of an active instrumented object